The dihydroazulene/vinylheptafulvene (DHA/VHF) photocouple is a promising candidate for molecular solar heat batteries, storing and releasing energy in a closed cycle. Much work has been done on improving the energy storage capacity and the half-life of the high-energy isomer via substituent functionalization, but similarly important is keeping these improved properties in common polar solvents, along with being soluble in these, which is tied to the dipole properties. However, the number of possible derivatives makes an overview of this combinatorial space impossible both for experimental work and traditional computational chemistry. Due to the time-consuming nature of running many thousands of computations, we look to machine learning, which bears the advantage that once a model has been trained, it can be used to rapidly estimate approximate values for the given system. Applying a convolutional neural network, we show that it is possible to reach good agreement with traditional computations on a scale that allows us to rapidly screen tens of thousands of the DHA/VHF photocouple, eliminating bad candidates and allowing computational resources to be directed toward meaningful compounds.