In this paper, we present a machine learning-based framework for predicting exciton coupling parameters which generalizes across a wide range of molecules. Our scheme is based upon a graph neural network (GNN) model for predicting ATCs for individual atoms within molecules (z i in equation 1). This GNN was trained using a database of 99,784 atoms belonging from 1843 molecules obtained from 1000 crystals. Exciton coupling values for pairs of molecules are then computed according to equation 1. The effectiveness of our framework is demonstrated by applying it to several fusedring electron acceptor (FREA) molecules, which are attracting considerable attention as materials for high-efficiency organic solar cells. For these cases, we succeed to calculate exciton diffusion coefficients using the coupling parameters predicted from our framework and kinetic Monte Carlo simulation. We also show that the coupling values can be predicted accurately for many of types of molecules found in a typical organic crystal structure database. We show that for the exceptional cases where prediction fails, a large cumulative error arises when propagating the predicted ATC values through the formula in equation 1. Our work therefore overcomes the restriction of previous machine-learned models of exciton coupling -the restriction to specific molecule types -and also provides a key component for a rational design methodology for organic semiconductors based upon high-throughput calculations.