In this work, we have developed a machine learning filter for predicting the internal reorganization energy of organic electronic materials. At present, we find the accuracy to be greater for compounds with low reorganization energy -as such it proves more useful for ignoring compounds expected to have high barriers for charge transport than as a fully accurate surrogate across the entire range considered. Nevertheless, since key applications require efficient charge transport, and thus low reorganization energies, we demonstrate its use in efficiently screening a pool of possible copolymers. Finally, we discuss frequent chemical motifs among compounds with low predicted reorganization energies.