Ensuring synthesizability remains a major challenge in generative small molecule design. While recent developments in synthesizable molecule generation have demonstrated promising results, they are largely confined to 2D molecular graph space, limiting their ability to perform shape- or protein target-conditioned generation. In this work we present SynCoGen, a simultaneous masked graph diffusion and flow matching model for synthesizable molecule generation. SynCoGen samples from the joint distribution of molecular building blocks, chemical reactions, and atomic coordinates. To train the model, we construct a dataset of over 600K synthesis‑aware building block graphs and 3.3M conformers. We show that SynCoGen achieves state-of-the-art performance on unconditional small molecule graph and conformer generation, and demonstrate the model's capability in linker design tasks. Overall, this approach represents a foundation for future applications enabled by non-autoregressive molecular graph generation, including structure conditioning, molecular optimization and analogue design.