Conductive polymer composite sensors have shown great potential in identifying gaseous analytes. To more thoroughly understand the physical and chemical mechanism of this type of sensors, a model was developed by combining two sub-models: a conductivity model and a thermodynamic model, which gives a relationship between the vapor concentration of analyte(s) and the change of the sensor signals. In this work, 64 chemiresistors representing eight different carbon concentrations (8–60 vol.% carbon) were constructed by depositing thin films of a carbon black–polyisobutylene composite onto concentric spiral platinum electrodes on a silicon chip. The responses of the sensors were measured in dry air and at various vapor pressures of toluene and trichloroethylene. Three parameters in the conductivity model were determined by fitting the experimental data. It was shown that by applying this model, the sensor responses can be predicted if the vapor pressure is known; furthermore the vapor concentration can be estimated based on the sensor responses. This model will guide the improvement of the design and fabrication of conductive polymer composite sensors for detecting and identifying organic vapors. A novel method was developed to optimize the selection of polymeric materials to be used within a chemiresistor array for anticipated samples without performing preliminary experiments. It is based on the theoretical predicted responses of chemiresistors and the criterion of minimizing the mean square error (MSE) of the chemiresistor array. After the number of chemiresistors to be used in an array and the anticipated sample chemistry are determined, the MSE values of all combinations of the candidate chemiresistors are calculated. The combination which has the minimum MSE value is the best choice. This can become computationally intensive for selection of polymers for large arrays from candidates in a large database. The number of combinations can be reduced by using the branch and bound method to save computation time. This method is suitable for samples at low concentrations where thermodynamic multi-component interactions are linear. To help users apply this polymer selection method for the sensors, a website including 10 solvents and 10 polymers was developed. Users can specify a target sample and obtain the best set of polymers for a sensor array to detect the sample. The activities of trichloroethylene and toluene in polyisobutylene were measured at very low concentrations. The activities for toluene are consistent with published values at higher concentrations. The values for trichloroethylene are a new contribution to the literature.