Organic memristors are promising molecular electronic devices for neuro-inspired on-chip learning applications. In this paper, we present a numerically efficient compact model suitable for Fe(bpy)2+3 organic memristors operating according to an intramolecular charge transfer switching mechanism. This compact model, being physics-based and relying on electrical characterizations and parametric extractions performed on test structures, is especially efficient in pulsed mode and describes the conductance variations for both SET and RESET regimes. Using this model, a dynamic multiscale simulation approach has been set-up to extend the model from individual devices to larger model systems that learn progressively through time. To verify the soundness and highlight emergent properties of the organic memristors, instances of the compact model have been simulated within a simple neuromorphic design that co-integrates with CMOS neurons. In addition, a larger supervised learning system using the new compact model is demonstrated. These successful tests suggest our model might be of interest to neuromorphic designers.