A quantitative structure-property relationship (QSPR) investigation was performed to develop a mathematical link between molecular structure and the clearing temperature of a series of structurally related liquid crystals. Molecular structures were encoded by a series of numerical descriptors encoding information regarding size, shape, and the ability to participate in intermolecular interactions. A genetic algorithm feature selection routine was utilized to select high-quality subsets of these descriptors for use in computational neural network models. A successful 10-descriptor model was developed using 318 compounds with a root-mean-square error of 5.4 K for the clearing temperature for the compounds in an external prediction set not used in model development.